Difference Between Hadoop vs SQL
The enormous data generated every second via Social Media like Facebook, Twitter, Instagram has paved the way for the development of Hadoop/ Big Data Ecosystem. The term “3V” referring to Volume, Velocity, and Veracity defines the importance of Hadoop to handle the streaming Data. Today, data are generated from multiple sources which are needed to be integrated for various purposes like Reporting, Data Analysis, and Research and for centralized storage of data. When we are doing so, there arise numerous issues with Traditional approaches like space, access, integrity, structure, and bandwidth. To handle all these issues, Hadoop provides a framework that enables to process the data with huge size, to provide easy access, high availability and to load data dynamically. It has a Distributed File System framework (HDFS) for the storage of Data and In-Built query processor called “Map Reduce” for the analysis and processing of data stored in HDFS.
Head to Head Comparison Between Hadoop and SQL (Infographics)
Below is the top 6 difference between Hadoop and SQL:
Key Differences Between Hadoop and SQL
Below is the difference between Hadoop and SQL are as follows:
- Schema on WRITE vs READ
Generally in a traditional database, during data load/migration from one database to another, it follows schema on Write approach. This makes the data load process to get excited/ aborted and results in rejection of records due to a difference in the structure of the source and target tables, Whereas in Hadoop system- all the data are stored in HDFS and Data are centralized.
Hadoop framework is mainly used for Data Analytics process. Thus it supports all three categories of data i.e. Structured, semi-structured and unstructured data and it enables Schema on reading approach.
- Structured data has a definite format. g.: XML file.
- Semi Structures data is looser; It might/ not have a schema. g.: Spreadsheet
- Unstructured data does not have a specific structure or a schema. E.g.: Plain Text or Image.
Hadoop works efficiently with unstructured data as it has the capability to interpret the data during the processing time.
| Approach | Advantage | Disadvantage |
| Schema on Write | · Pre-defined Structures
· Faster Read. E.g.: Traditional RDBMS. |
Slow Data Load
High Latency |
| Schema on Read | · Dynamic Structure
· Fast write and read. E.g.: Hadoop |
Fast Data load
Low Latency |
Table: Schema on WRITE VS Schema on READ.
- Scalability & Cost
Hadoop Framework is designed to process a large volume of data. Whenever the size of data increases, a number of additional resources like data node can be added to the cluster very easily than the traditional approaching of static memory allocation. Time and Budget is relatively very less for implementing them and also Hadoop provides Data Locality where the data is made available in the node that executed the job.
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- Fault Tolerance
In the traditional RDBMS, when data is lost due to corruption or any network issue, it takes more time, cost and resource to get back the lost data. But, Hadoop has a mechanism where the data has minimum three level of replication factor for the data that are stored in HDFS. If one of the data nodes that hold data gets failed, data can be easily pulled from other data nodes with high availability of data. Hence makes the data readily available to user irrespective of any failure.
- Functional Programming
Hadoop supports writing functional programming in languages like java, scala, and python. For any application that requires any additional functionality can be implemented by registering UDF –User Defined Functions in the HDFS. In RDBMS, there is no possibility of writing UDF and this increases the complexity of writing SQL. Moreover the data stored in HDFS can be accessed by all the ecosystem of Hadoop like Hive, Pig, Sqoop and HBase. So, if the UDF is written it can be used by any of the abovementioned application. It increases the performance and supportability of the system.
- Optimization
Hadoop stores data in HDFS and Process though Map Reduce with huge optimization techniques. The most popular techniques used for handling data are using partitioning and bucketing of the data stored. Partitioning is an approach for storing the data in HDFS by splitting the data based on the column mentioned for partitioning. When the data is injected or loaded into HDFS, it identifies the partition column and pushes the data into the concerned partition directory. So the query fetches the result set by directly fetching the data from the partitioned directory. This reduces the whole table scan, improves the response time and avoids latency.
Another approach is called Bucketing of the data. This enables the analyst to easily distribute the data among the data nodes. All nodes will have an equal number of data distributed. The bucketing column is selected in such a way that it has the least number of cardinality.
These approaches are not available in the Traditional method of SQL.
- Data Type
In a traditional approach, the datatype supported are very limited. It supports only structured data. Thus to clean and format the schema of data itself will take more time. But, Hadoop supports complex data type like Array, Struct, and Map. This encourages using the different kinds of a dataset to be used for data load. For Ex: the XML data can be loaded by defining the data with XML elements containing complex data type.
- Data Compression
There are very less inbuilt compression techniques available for the traditional database system. But for the Hadoop framework, there are many compression techniques like gzib, bzip2, LZO and snappy. The default compression mode is LZ4. Even the tables can be compressed using the compression techniques like Parquet, ORC. Compression techniques help in making the tables to occupy very less space increase the throughput and faster query execution.
Hadoop vs SQL Comparison Table
Following is the Comparison table between Linux and Solaris.
| Characteristics | Traditional SQL | Hadoop |
| Data Size | Gigabytes | Petabytes |
| Access | Interactive & Batch | Batch |
| Updates | Read and Write – Multiple times | Write once, read Multiple times |
| Structure | Static Schema | Dynamic Schema |
| Integrity | High | Low |
| Scaling | Non-Linear | Linear |
Table: Comparison between Traditional Hadoop and SQL Framework.
Conclusion
Overall, Hadoop steps ahead of the traditional SQL in terms of cost, time, performance, reliability, supportability and availability of data to the very large user group. In order to efficiently handle the tremendous amount of data generated every day, Hadoop framework helps in timely capturing, storing, processing, filtering and finally storing in it in a centralized place.
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